Search Results (17)

Semantic segmentation and height estimation tasks in remote sensing imagery exhibit distinctive characteristics, including scale sensitivity, category imbalance, and insufficient fine details. Recent approaches have leveraged multi-task learning methods to jointly predict these tasks along with auxiliary tasks, such as edge detection, to improve the accuracy of fine-grained details. However, most approaches only acquire knowledge from auxiliary tasks, disregarding the inter-task knowledge guidance across all tasks. To address these challenges, we propose KMNet, a novel architecture referred to as a knowledge-guided multi-task network, which can be applied to different primary and auxiliary task combinations. KMNet employs a multi-scale methodology to extract feature information from the input image. Subsequently, the architecture incorporates the multi-scale knowledge-guided fusion (MKF) module, which is designed to generate a comprehensive knowledge bank serving as a resource for guiding the feature fusion process. The knowledge-guided fusion feature is then utilized to generate the final predictions for the primary tasks. Comprehensive experiments conducted on two publicly available remote sensing datasets, namely the Potsdam dataset and the Vaihingen dataset, demonstrate the effectiveness of the proposed method in achieving impressive performance on both semantic segmentation and height estimation tasks. Codes, pre-trained models, and more results will be publicly available. Full article

In this study, the effect of growing conditions in different harvest years on the physicochemical properties of various Ivorian mango kernel fat (MKF) varieties was investigated. The fats extracted from mango kernels were analysed with respect to their fatty acid composition (FAC) and triacylglycerol (TAG) composition, melting profile, and solid fat content (SFC). The results indicate that variations in MKF content between non-consecutive harvest years (2021 and 2023) were influenced by environmental conditions, particularly rainfall and genetic factors, demonstrating the diverse response to environmental changes. Traditional varieties showed a decrease in fat content in the drier year (2023), while commercial varieties exhibited an increase. FAC was also affected, with changes in oleic- and stearic-acid levels, depending on water availability. This impacted the TAG composition, which in turn influenced the physical characteristics of the MKF. These findings highlight the importance of climatic factors in determining the quality and characteristics of MKF, which have significant implications for industrial applications. This suggests that it is necessary to take into account such factors when implementing logistic chains for the supply of quality raw materials. Full article

As the world grapples with the challenges posed by climate change and depleting energy resources, achieving sustainability in the construction and operation of buildings has become a paramount concern. The construction and operation of buildings account for a substantial portion of global energy consumption and carbon emissions. Hence, the accurate prediction of building energy consumption is indispensable for reducing energy waste, minimizing greenhouse gas emissions, and fostering sustainable urban development. The aspiration to achieve predicted outcomes with remarkable accuracy has emerged as a pivotal objective, coinciding with the burgeoning popularity of deep learning techniques. This paper presents an auto-evaluation model for building energy consumption prediction via Long Short-Term Memory with modified Kalman filtering (LSTM-MKF). Results gleaned from data validation activities evince a notable transformation—a reduction of the maximal prediction error from an initial 83% to a markedly ameliorated 24% through the intervention of the proposed model. The LSTM-MKF model, a pioneering contribution within this paper, clearly exhibits a distinct advantage over the other models in terms of predictive accuracy, as underscored by its superior performance in all three key metrics, including mean absolute error, root mean square error, and mean square error. The model presents excellent potential as a valuable tool for enhancing the precision of predictions of building energy consumption, a pivotal aspect in energy efficiency, smart city development, and the formulation of informed energy policy. Full article

Red palm oil (RPO), which is rich in micronutrients, especially carotenoids, is different from its deodorized counterpart, palm oil. It is considered as one of the most promising food ingredients, owing to its unique compositions and nutritional values, while its usage could be further developed by improving its thermal behaviors. In this article, two typical commercial RPOs, HRPO (H. red palm oil) and NRPO (N. red palm oil), were evaluated by analyzing their fatty acids, triacylglycerols, micronutrients, oxidative stability index (OSI), and solid fat contents (SFCs). Micronutrients, mainly carotenes, tocopherols, polyphenols, and squalene, significantly increased the oxidative stability indices (OSIs) of the RPOs (from 10.02 to 12.06 h), while the OSIs of their micronutrient-free counterparts were only 1.12 to 1.82 h. HRPO exhibited a lower SFC than those of NRPO. RPOs softened at around 10 °C and completely melted near 20 °C. Although the softening problem may limit the usages of RPOs, that problem could be solved by incorporating RPOs with mango kernel fat (MKF). The binary blends containing 40% RPOs and 60% MKF exhibited desirable compatibilities, making that blend suitable for the manufacture of aerated emulsions with improved whipping performance and foam stabilities. The results provide a new application of RPOs and MKF in the manufacture of aerated emulsions with improved nutritional values and desired whipping capabilities. Full article

This article presents a new prediction model, the ordinary differential equations–memory kernel function (ODE–MKF), constructed from multiple backtracking initial values (MBIV). The model is similar to a simplified numerical model after spatial dimension reduction and has both nonlinear characteristics and the low-cost advantage of a time series model. The ODE–MKF focuses on utilizing more temporal information and includes machine learning to solve complex mathematical inverse problems to establish a predictive model. This study first validates the feasibility of the ODE–MKF via experiments using the Lorenz system. The results demonstrate that the ODE–MKF prediction model could describe the nonlinear characteristics of complex systems and exhibited ideal predictive robustness. The prediction of the El Niño-Southern Oscillation (ENSO) index further demonstrates its effectiveness, as it achieved 24-month lead predictions and effectively improved nonlinear problems. Furthermore, the reliability of the model was also tested, and approximately 18 months of prediction were achieved, which was verified with the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) radiation fluxes. The short-term memory index Southern Oscillation (SO) was further used to examine the applicability of ODE–MKF. A six-month lead prediction of the SO trend was achieved, indicating that the predictability of complex systems is related to their inherent memory scales. Full article

Structural health monitoring (SHM) is vital for ensuring the service safety of aging bridges. As one of the most advanced sensing techniques, Global Navigation Satellite Systems (GNSS) could capture massive spatiotemporal information for effective bridge structural health monitoring (BSHM). Unfortunately, GNSS measurements often contain outliers due to various factors (e.g., severe weather conditions, multipath effects, etc.). All such outliers could jeopardize the accuracy and reliability of BSHM significantly. Previous studies have examined the feasibility of integrating the conventional multi-rate Kalman filter (MKF) with an adaptive algorithm in the data processing processes to ensure BSHM accuracy. However, frequent parameter adjustments are still needed in tedious data processing processes. This study proposed an outlier detection method using a Nelder-Mead simplex robust multi-rate Kalman filter (RMKF) for supporting trustworthy BSHM using GNSS and accelerometer. In the end, the authors have validated the proposed method using the monitoring data collected at the Wilford Bridge in the UK. Results showed that the accuracy of the total dynamic vibration displacement time series has been improved by 21% compared with the results using the conventional MKF approach. The authors envision that the proposed method will shed light on reliable and explainable data processing policy and trustworthy BSHM. Full article

As a biodegradable thermoplastic, polylactic acid (PLA) shows great potential to replace petroleum-based plastics. Nevertheless, the flammability and brittleness of PLA seriously limits its use in emerging applications. This work is focused on simultaneously improving the flame-retardancy and toughness of PLA at a low additive load via a simple strategy. The PLA/MKF/NTPA biocomposites were prepared by incorporating alkali-treated, lightweight, renewable kapok fiber (MKF) and high-efficiency, phosphorus-nitrogenous flame retardant (NTPA) into the PLA matrix based on the extrusion–injection molding method. When the additive loads of MKF and NTPA were 0.5 and 3.0 wt%, respectively, the PLA/MKF/NTPA biocomposites (PLA3.0) achieved a rating of UL-94 V-0 with an LOI value of 28.3%, and its impact strength (4.43 kJ·m⁻²) was improved by 18.8% compared to that of pure PLA. Moreover, the cone calorimetry results confirmed a 9.7% reduction in the average effective heat of combustion (av-EHC) and a 0.5-fold increase in the flame retardancy index (FRI) compared to the neat PLA. NTPA not only exerted a gas-phase flame-retardant role, but also a condensed-phase barrier effect during the combustion process of the PLA/MKF/NTPA biocomposites. Moreover, MKF acted as an energy absorber to enhance the toughness of the PLA/MKF/NTPA biocomposites. This work provides a simple way to prepare PLA biocomposites with excellent flame-retardancy and toughness at a low additive load, which is of great importance for expanding the application range of PLA biocomposites. Full article

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI. Full article

The roundhouse kick is the most commonly executed kick in Taekwondo competitions. Because it is the fastest kick among kicking techniques and can easily change its motion on the basis of the opponent’s motion, the roundhouse kick is suitable for scoring points. To explore the functional role of segmental movements during the roundhouse kick, this study aimed to investigate the effects of various footwork techniques on biomechanical contributions of body segments to toe velocity during roundhouse kicks. Ten elite Taekwondo athletes performed roundhouse kicks with different footwork positions. The contributions of body segments to the toe velocity were obtained through 3D motion analysis. Results were compared by repeated measures analysis of variance (ANOVA), and there were significant differences in the contributions of whole-body movement, trunk movement, trunk rotation, and toe velocity. It was concluded that in the minimum knee flexion (MKF) event, for all types of footwork, thigh rotation showed the most significant contribution (51–64.4%), followed by trunk rotation (16.7–29.9%) and whole-body movement (5.1–13.4%). However, in the impact event, the contribution of thigh rotation decreased, while the contribution of calf rotation increased significantly. Full article

The novelty of this work is the use of two mango by-products, mango peel and kernel, to obtain an extruded food. As well as the development of this food through a design of mixtures, we conducted sensorial analysis of the food through a hedonic test, in order not only to develop an extruded food with mango by-products, but also to develop a food that will be accepted by the consumer. A simple lattice mixture design was carried out with 14 mixtures, where the components were white corn flour (WCF), mango peel flour (MPF) and mango kernel flour (MKF), both from the Tommy Atkins mango variety. Physical and chemical properties such as the expansion index (EI), hardness, water absorption index (WAI), water solubility index (WSI), total phenols, DPPH and ABTS were evaluated. An optimization region was found that included 3 design points. Mixtures 1, 6 and 12 were evaluated using a nine-point hedonic scale to determine the acceptability of the product. Appearance, taste, and texture of the extrudates was evaluated. The extrudate with the best overall acceptability and the optimum physical and chemical properties contained 58.33% white corn flour, 33.33% mango peel flour and 8.33% mango kernel flour. Full article

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods. Full article

The instantaneous frequency (IF) is a vital parameter for the analysis of non-stationary multicomponent signals, and plays an important role in space cone-shaped target recognition. For a cone-shaped target, IF estimation is not a trivial issue due to the proximity of the energy of the IF components, the intersections among different IF components, and the existence of noise. Compared with the general parameterized time-frequency (GPTF), the traditional Kalman filter can perform better when the energy of different signal components is close. Nevertheless, the traditional Kalman filter usually makes association mistakes at the intersections of IF components and is sensitive to the noise. In this paper, a novel IF estimation method based on modified Kalman filter (MKF) is proposed, in which the MKF is used to associate the intersecting IF trajectories obtained by the synchroextracting transform (SET). The core of MKF is the introduction of trajectory correction strategy in which a trajectory survival rate is defined to judge the occurrence of association mistakes. When the trajectory survival rate is below the predetermined threshold, it means that an association mistakes occurs, and then the new trajectories generated by the random sample consensus algorithm are used to correct the wrong associations timely. The trajectory correction strategy can effectively obviate the association mistakes caused by the intersections of IF components and the noise. The windowing technique is also used in the trajectory correction strategy to improve computational speed. The experimental results based on the electromagnetic computation data show that the proposed method is more robust and precise than the traditional Kalman filter. Moreover, the proposed method has great performance advantages compared with other methods (i.e., the multiridge detection, the ant colony optimization, and the GPTF methods) especially in the case of low signal noise ratio (SNR). Full article

Navigation is a precondition for ocean space vehicles to work safely in polar regions. The traditional polar algorithms employ the grid strapdown inertial navigation system (SINS) as the backbone and Doppler velocity log (DVL) output velocity as measurements to constitute the integrated navigation system, of which, however, the position errors still accumulate with time. The ultra-short baseline (USBL) position system can provide position information that can be used to improve the performance of the SINS/DVL integrated system. Therefore, a grid SINS/DVL/USBL integrated algorithm for polar navigation is proposed in this paper. In order to extend the availability of the USBL and improve integration accuracy in polar regions, the USBL observation model is established based on the relative position measurement firstly. Then, a grid SINS/DVL/USBL integrated algorithm is proposed to fuse the information of these sensors with a modified Kalman filter (MKF) dealing with the sparse USBL output. Finally, a vector fault detection method, which takes the measurements as detection objects instead of the filter, is designed to locate the measurement fault and can be employed by the centralized filter to improve the fault-tolerant. Simulation and experiment results show that the proposed grid SINS/DVL/USBL integrated navigation system can further restrain SINS errors especially the position errors effectively. Meanwhile, the vector fault detection method can detect and isolate the fault measurements of centralized filter immediately and accurately. Therefore, the proposed fault-tolerant grid SINS/DVL/USBL integrated navigation algorithm can improve the reliability and accuracy of polar navigation for ocean space application. Full article

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is crucial to the management and planning of water resources for arid and semi-arid areas, for which LE estimations from different satellite sensors unfortunately often contain data gaps and are inconsistent. Many integration approaches have been implemented to overcome these limitations; however, most suffer from either the persistent bias of relying on datasets at only one resolution or the spatiotemporal inconsistency of LE products. In this study, we exhibit an integration case in the midstream of the Heihe River Basin of northwest China by using a multi-resolution Kalman filter (MKF) method to develop continuous and consistent LE maps from satellite LE datasets across different resolutions. The Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16), the Landsat-based LE product derived from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor, and ground observations of eddy covariance flux tower from June to September 2012 are used. The integrated results illustrate that data gaps of MOD16 dropped to less than 0.4% from the original 27–52%, and the root-mean-square error (RMSE) between the LE products decreased by 50.7% on average. Our findings indicate that the MKF method has excellent capacity to fill data gaps, reduce uncertainty, and improve the consistency of multiple LE datasets at different resolutions. Full article

State estimation is crucial for robot autonomy, visual odometry (VO) has received significant attention in the robotics field because it can provide accurate state estimation. However, the accuracy and robustness of most existing VO methods are degraded in complex conditions, due to the limited field of view (FOV) of the utilized camera. In this paper, we present a novel tightly-coupled multi-keyframe visual-inertial odometry (called VINS-MKF), which can provide an accurate and robust state estimation for robots in an indoor environment. We first modify the monocular ORBSLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping) to multiple fisheye cameras alongside an inertial measurement unit (IMU) to provide large FOV visual-inertial information. Then, a novel VO framework is proposed to ensure the efficiency of state estimation, by adopting a GPU (Graphics Processing Unit) based feature extraction method and parallelizing the feature extraction thread that is separated from the tracking thread with the mapping thread. Finally, a nonlinear optimization method is formulated for accurate state estimation, which is characterized as being multi-keyframe, tightly-coupled and visual-inertial. In addition, accurate initialization and a novel MultiCol-IMU camera model are coupled to further improve the performance of VINS-MKF. To the best of our knowledge, it’s the first tightly-coupled multi-keyframe visual-inertial odometry that joins measurements from multiple fisheye cameras and IMU. The performance of the VINS-MKF was validated by extensive experiments using home-made datasets, and it showed improved accuracy and robustness over the state-of-art VINS-Mono. Full article